Data and Statistics
Data and statistical analysis in the Asia and Pacific region contribute to knowledge generation in ADB, helping strengthen its institutional priorities and operational effectiveness in its developing member economies.
Sectors in the Asia and the Pacific region have become increasingly integrated and production processes are more complex than ever. ADB compiles Input-Output Tables (IOTs) that provide comprehensive and granular insights on these economic relationships and enable the derivation of relevant production, trade and value chains statistics.
IOTs are fundamentally composed of three matrices representing interindustry transactions (intermediate consumption block), output purchases of final consumers such as households, government, and non-profit institutions from industries (final demand block), and payments to primary factors of production as well as net taxes (value-added block). These are structured in such a way that the market-clearing condition of quantity demanded being equal to quantity supplied holds for each industry.
Supply and use tables (SUTs) are an integrated statistical estimation and economic analysis model that facilitates the compilation of more reliable, consistent, and internationally comparable estimates of key economic statistics such as gross domestic product. The improved and more detailed statistics are expected to provide a better basis for measuring economic output and growth; facilitating informed policy making; and monitoring progress toward the Sustainable Development Goals (SDGs), relating especially to poverty alleviation, economic growth, technological progress, and industrial and infrastructure development.
ADB compiles supply and use tables (SUTs) and input-output tables (IOTs) across several participating members.
Data was last updated on June 2023.
The input-output (IO) analytical framework provides a systematic approach for studying interrelationships of an economy’s industries through their use of each other’s products as inputs to production for their respective outputs. The main data source for this type of macroeconomic analysis is an IO Table, which describes flows of goods and services that took place in a specific geographic region and a particular accounting period.
The most common application of the IO analytical framework answers the question, “how much output must be produced by each industry in the economy given specific levels of final demand?” IO analysis is able to quantify both direct and indirect output requirements that result from back-and-forth transactions of industries to meet this demand using the Leontief inverse or the total requirements matrix.
IO analysis allows researchers to identify key sectors in an economy through IO-based multipliers and elasticities across different dimensions such as income, employment, energy use, and environmental impact. It also makes decomposition of economic transactions either from the perspective of contributions or dependencies organized and tractable.
ADB provides timely data and analytical reports that apply the IO analytical framework, alongside capacity-building activities and user-friendly programs, as valuable resources for evidence-based, cross-cutting policymaking.
The Input-Output framework can be employed to analyze the interdependencies of various sectors within the domestic economy, as measured by economic linkages. For example, one might be interested in examining the economic linkages of the tourism sector vis-à-vis agriculture, industry, and other services sector. The scattered chart compares the normalized forward and normalized backward linkages of various sectors, with point size reflecting their respective value-added contribution.
On one hand, tourism sector may purchase its production inputs from a more upstream industry, say agriculture. This interconnection between the tourism sector (as a purchaser) and agriculture sector (as a supplier of tourism sector’s production inputs) is indicated by backward linkage. On the other hand, the output of the tourism sector may also be used as production inputs of a more downstream industry, say other services. The relationship between the tourism sector (as a supplier of inputs) and other services (as a purchaser) is in indicated by forward linkage. A comparison of the strengths of backward and forward linkages allows the identification of key sectors that are most interconnected with the other sectors, and this could be used as a guide in policy decisions.
Backward and forward linkages can also be used to characterize the sectors into four typologies, which describe how connected they are with the rest of the economy. These typologies characterize the various sectors in the economy as (1) generally dependent, (2) dependent on interindustry demand, (3) generally independent, or (4) dependent on interindustry supply. Analysis of economic linkages is relevant in determining the potential impact of certain policies to the overall economy, and is therefore an important tool in crafting informed policy decisions.
Source: R. Miller and P. Blair. 2009. Input–Output Analysis: Foundations and Extensions. Cambridge: Cambridge University Press.
In a global economic environment increasingly characterized by fragmented and internationally distributed production processes and by trade in intermediates, the necessity for pertinent quantitative information of sufficient granularity is more urgent than ever. The input-output economic analysis framework provides an ideal system for depicting and analyzing productive trading activities which are so integrated in the globalized world.
To address the increasing demand for such information and specifically to facilitate analysis work related to the Asia and the Pacific Region, the Asian Development Bank (ADB) has produced Multiregional Input-Output (MRIO) Tables building on the World Input-Output Database (Timmer et al. 2015) to include 29 Asian economies, namely: Armenia, Australia, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, Fiji, Georgia, Hong Kong, China, India, Indonesia, Kazakhstan, Kyrgyz Republic, Lao People's Democratic Republic, Malaysia, Maldives, Mongolia, Nepal, New Zealand, Pakistan, People's Republic of China, Philippines, Republic of Korea, Singapore, Sri Lanka, Taipei, China, Thailand, and Viet Nam. This has facilitated the production and analysis of global value chain related statistics for Asian economies. Economies explicitly identified in the ADB MRIO account for at least 93% of the world Gross Domestic Product.
ADB compiles MRIO tables at current prices, and at constant 2010 prices for 62 economies and aggregated Rest of the World. More recent tables at current prices will cover 72 economies in addition to the Rest of the World.
Access to the complete datasets is available upon request. Click here for more information.
ADB compiles and visualizes indicators and model-based results to form comprehensive profiles for each economy, based on the strengths and characteristics of the economy’s respective input-output linkages.
On the production side, sectoral composition is described using the shares of each sector’s value-added in the total gross value-added (GVA) of the economy. This shows which activities the economy generates most of its income from.
Across all subregions, the business services sector had the highest share in GVA: 37.6% in 2000 and 45.8% in 2018. In absolute terms, business services in East Asia had the highest value-added among all the subregional groups for both years, even though the sector’s share within the subregion decreased from 43.9% in 2000 to 39.7% in 2018 (Figure 1.2).
Meanwhile, the shares of business services sectors in the respective GVAs of South and Central Asia, Southeast Asia and the Pacific, and East Asia increased significantly from 2000 to at least a 40.0% share in 2018. On average, while the shares of the primary, low tech, and public and personal services sectors decreased slightly, the share of the high and medium tech sector decreased substantially (from 15.5% in 2000 to 9.7% in 2018).
In Asia and the Pacific, output multipliers are particularly strong in industries that contemporary economies consider as “critical infrastructure” industries. These sectors include the light and heavy manufacturing, utilities sectors and, to a lesser but still significant extent, sectors involved in travel and connectivity, such as hotels and restaurants, transport services, and telecommunications.
The multipliers indicate that, on average, a $1 demand in any of these sectors could generate up to $1.55 worth of production. The extra $0.55 is attributable to the indirect production required throughout the economy, since supplying sectors themselves demand inputs as well.
In this sense, the intersectoral demand generated within production chains “multiplies” the direct production required to satisfy a dollar worth of demand.
Since 2000, dynamic changes in the structure of economies have been observed—there has been a shift from commodity-based production to more industrial and service-oriented activities and a rise in demand led by private consumption and exports. There have also been changes in the complexities of production technologies within economies. In a closed input–output system, shifts in demand induce changes in the supply-side components of the economy and vice versa. The range of indicators present the different channels through which changes in one sector may reverberate throughout the economy.
Among its many analytical contributions, input–output analysis can also be used to evaluate the impact of critical changes in the macroeconomy, such as demand shocks or rising labor costs, on the overall production levels of the economy. In particular, within an input–output system, an economy is modeled such that the demand in one of its sectors could impact demands (and therefore outputs) in other sectors, through existing connections between them at one or more levels.
When one reads that Japan had exported $15.2 billion worth of goods and services to India in 2019, one imagines $15.2 billion worth of Japanese goods and services going over to be consumed by Indians. But this is not the case. Some Japanese exports contain imported inputs—from Viet Nam, from Germany, even from India itself. Some of the exports that India receives, meanwhile, are processed and then re-exported—to Singapore, to Canada, perhaps even back to Japan. Indeed, Japanese value-added actually sent to and consumed in India was only $14.8 billion.
Such activities are the mark of global value chains (GVCs). These involve the fragmentation of production into several stages undertaken in several territories. This new form of globalization intensified in the 1990s following improvements in information and communication technologies and the spread of market economies in the former Communist world. They have allowed economies to specialize in tasks (assembly, marketing, business processing) rather than in products (automobiles, computers, appliances), allowing them to avoid having to build an entire value chain from scratch. At the same time, the disruptions and risks they have brought mean GVCs are not without their challenges.
In the age of global value chains, research on trade can no longer simply rely on gross export statistics. To disentangle the various forms trade can take, an accounting framework that carefully decomposes exports into meaningful value-added categories is necessary. ADB employs this framework in compiling its GVC statistics, drawing from data in the MultiRegional Input-Output (MRIO) tables. A more comprehensive exposition of this framework is provided in the GVC chapter of Key Indicators for Asia and the Pacific, or see the available charts at the Key Indicators Database